text: # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Tests for gradients of (block) LSTM/GRU operations."""
  
  import functools
  
  import numpy as np
  
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_rnn_ops
  from tensorflow.python.ops import gradients
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import rnn_grad  # pylint: disable=unused-import
  from tensorflow.python.platform import test
  
  
  class RNNGradTest(test.TestCase):
  
    @test_util.deprecated_graph_mode_only
    def testBlockLSTMV1V2Consistency(self):
      num_steps = 1
      batch_size = 1
      input_size = 1
      hidden_size = 8
      w = deterministic_random_uniform(
          [input_size + hidden_size, 4 * hidden_size])
      b = deterministic_random_uniform([4 * hidden_size])
      x = deterministic_random_uniform([num_steps, batch_size, input_size])
      cs_prev = h_prev = deterministic_random_uniform([batch_size, hidden_size])
  
      all_cs, all_h = self._lstm_block(
          functools.partial(
              gen_rnn_ops.BlockLSTM,
              forget_bias=0.0,  # Disable to match V2 default.
              cell_clip=0.0),  # Disable to match V2 default.
          w, b, x, cs_prev, h_prev)
      w_grad, b_grad = gradients.gradients(all_cs + all_h, [w, b])
  
      w_ifco, b_ifco = icfo_to_ifco(w, b)
      all_cs_ifco, all_h_ifco = self._lstm_block(
          gen_rnn_ops.BlockLSTMV2, w_ifco, b_ifco, x, cs_prev, h_prev)
      w_ifco_grad, b_ifco_grad = gradients.gradients(
          all_cs_ifco + all_h_ifco, [w_ifco, b_ifco])
  
      self.assertAllEqual(all_cs, all_cs_ifco)
      self.assertAllEqual(all_h, all_h_ifco)
      self.assertAllEqual(w_grad, w_ifco_grad)
      self.assertAllEqual(b_grad, b_ifco_grad)
  
    @test_util.deprecated_graph_mode_only
    def testLSTMBlockCell(self):
      batch_size = np.random.randint(1, 32)
      input_size = np.random.randint(1, 32)
      hidden_size = np.random.randint(1, 32)
      w = deterministic_random_uniform(
          [input_size + hidden_size, 4 * hidden_size])
      b = deterministic_random_uniform([4 * hidden_size])
      x = deterministic_random_uniform([batch_size, input_size])
      cs_prev = h_prev = deterministic_random_uniform([batch_size, hidden_size])
      w_peephole = array_ops.zeros(cs_prev.shape[1:], dtype=w.dtype)
      cs_grad = deterministic_random_uniform([batch_size, hidden_size])
      h_grad = deterministic_random_uniform([batch_size, hidden_size])
  
      outputs = []
      grads = []
      for use_gpu in [False, True]:
        with self.cached_session(use_gpu=use_gpu):
          output = gen_rnn_ops.lstm_block_cell(
              x=x,
              cs_prev=cs_prev,
              h_prev=h_prev,
              w=w,
              wci=w_peephole,
              wcf=w_peephole,
              wco=w_peephole,
              b=b,
              forget_bias=1.0,
              cell_clip=0.0,
              use_peephole=False)
          (i, cs, f, o, ci, co, _) = output
          grad = gen_rnn_ops.lstm_block_cell_grad(
              x=x,
              cs_prev=cs_prev,
              h_prev=h_prev,
              w=w,
              wci=w_peephole,
              wcf=w_peephole,
              wco=w_peephole,
              b=b,
              i=i,
              cs=cs,
              f=f,
              o=o,
              ci=ci,
              co=co,
              cs_grad=cs_grad,
              h_grad=h_grad,
              use_peephole=False)
          outputs.append(output)
          grads.append(grad)
      self.assertAllClose(outputs[0], outputs[1])
      self.assertAllClose(grads[0], grads[1])
  
    def _lstm_block(self, op, w, b, x, cs_prev, h_prev):
      w_peephole = array_ops.zeros(cs_prev.shape[1:], dtype=w.dtype)
      _, all_cs, _, _, _, _, all_h = op(
          seq_len_max=math_ops.cast(array_ops.shape(x)[0], dtypes.int64),
          x=x,
          cs_prev=cs_prev,
          h_prev=h_prev,
          w=w,
          wci=w_peephole,
          wcf=w_peephole,
          wco=w_peephole,
          b=b,
          use_peephole=False)
      return all_cs, all_h
  
  
  def deterministic_random_uniform(shape):
    return ops.convert_to_tensor(np.random.random(shape), dtype=dtypes.float32)
  
  
  def icfo_to_ifco(w, b):
    """Convert gates' weights and biases from ICFO to IFCO layout."""
    w_i, w_c, w_f, w_o = array_ops.split(w, num_or_size_splits=4, axis=1)
    b_i, b_c, b_f, b_o = array_ops.split(b, num_or_size_splits=4)
    w_ifco = array_ops.concat([w_i, w_f, w_c, w_o], axis=1)
    b_ifco = array_ops.concat([b_i, b_f, b_c, b_o], axis=0)
    return w_ifco, b_ifco
  
  
  if __name__ == "__main__":
    test.main()
  